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Metals 2019, 9(2), 155; https://doi.org/10.3390/met9020155

Classification and Quantitative Evaluation of Eddy Current Based on Kernel-PCA and ELM for Defects in Metal Component

1,2
,
1,2
,
1,2
,
1,2,*
and
1,2
1
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
2
Engineering Research Center for Mineral Pipeline Transportation of Yunnan Province, Kunming 650500, China
*
Author to whom correspondence should be addressed.
Received: 26 December 2018 / Revised: 17 January 2019 / Accepted: 28 January 2019 / Published: 1 February 2019
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Abstract

Eddy current testing technology is widely used in the defect detection of metal components and the integrity evaluation of critical components. However, at present, the evaluation and analysis of defect signals are still mostly based on artificial evaluation. Therefore, the evaluation of defects is often subjectively affected by human factors, which may lead to a lack in objectivity, accuracy, and reliability. In this paper, the feature extraction of non-linear signals is carried out. First, using the kernel-based principal component analysis (KPCA) algorithm. Secondly, based on the feature vectors of defects, the classification of an extreme learning machine (ELM) for different defects is studied. Compared with traditional classifiers, such as artificial neural network (ANN) and support vector machine (SVM), the accuracy and rapidity of ELM are more advantageous. Based on the accurate classification of defects, the linear least-squares fitting is used to further quantitatively evaluate the defects. Finally, the experimental results have verified the effectiveness of the proposed method, which involves automatic defect classification and quantitative analysis. View Full-Text
Keywords: eddy current testing; kernel principal component analysis; feature extraction; defect classification; quantitative analysis eddy current testing; kernel principal component analysis; feature extraction; defect classification; quantitative analysis
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Deng, W.; Ye, B.; Bao, J.; Huang, G.; Wu, J. Classification and Quantitative Evaluation of Eddy Current Based on Kernel-PCA and ELM for Defects in Metal Component. Metals 2019, 9, 155.

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